Challenges when identifying migration from geo-located Twitter data

Caitrin Armstrong, Ate Poorthuis, Matthew Zook, Derek Ruths, Thomas Soehl

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Given the challenges in collecting up-to-date, comparable data on migrant populations the potential of digital trace data to study migration and migrants has sparked considerable interest among researchers and policy makers. In this paper we assess the reliability of one such data source that is heavily used within the research community: geolocated tweets. We assess strategies used in previous work to identify migrants based on their geolocation histories. We apply these approaches to infer the travel history of a set of Twitter users who regularly posted geolocated tweets between July 2012 and June 2015. In a second step we hand-code the entire tweet histories of a subset of the accounts identified as migrants by these methods. Upon close inspection very few of the accounts that are classified as migrants appear to be migrants in any conventional sense or international students. Rather we find these approaches identify other highly mobile populations such as frequent business or leisure travellers, or people who might best be described as “transnationals”. For demographic research that draws on this kind of data to generate estimates of migration flows this high mis-classification rate implies that findings are likely sensitive to the adjustment model used. For most research trying to use these data to study migrant populations, the data will be of limited utility. We suspect that increasing the correct classification rate substantially will not be easy and may introduce other biases.

Original languageEnglish
Article number1
JournalEPJ Data Science
Issue number1
StatePublished - Dec 2021

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).


  • Global human mobility
  • Migration
  • Twitter

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Computational Mathematics


Dive into the research topics of 'Challenges when identifying migration from geo-located Twitter data'. Together they form a unique fingerprint.

Cite this